We are building Reflow, a workforce and workflow intelligence platform that helps teams understand and improve how work gets done. At the core of Reflow is a growing set of machine learning models that learn from real work patterns to predict outcomes, surface insights, and power intelligent automation.

WHAT YOU WILL DO

• Train, fine-tune, and evaluate machine learning models on real-world workflow and behavioral data

• Build predictive models for task outcomes, productivity trends, capacity forecasting, and workflow optimization

• Fine-tune large models and foundation models for domain-specific prediction, classification, and embedding tasks

• Design and maintain feature pipelines, training loops, and evaluation frameworks

• Work with engineers and product teams to integrate trained models into production systems

• Monitor model performance and iterate using offline evaluation and live data feedback

WHO YOU ARE

• Strong foundation in Python and applied machine learning

• Experience training supervised and self-supervised models

• Hands-on experience with model fine-tuning, evaluation, and deployment workflows

• Comfortable working end-to-end from raw data through training to production inference

• Pragmatic, curious, and experimental with a bias toward shipping working models

BONUS POINTS

• Experience fine-tuning large language models or embedding models

• Familiarity with PyTorch, TensorFlow, or similar frameworks

• Experience with time series forecasting, behavioral modeling, or graph-based learning

• Background working with messy, real-world product data

WHY JOIN

• Build the learning backbone of Reflow that turns work data into predictions and signals

• Work closely with founders, engineers, and product teams

• Ship real models into production and see them shape how teams work

• Flexible structure, part-time or full-time, with a focus on ownership and iteration speed

COMPENSATION

We offer competitive pay based on the market and where you’re located. The salary ranges in our job postings are intentionally wide because they need to cover both U.S. and international candidates. Our final offer will depend on things like your experience, skill set, and location.